Gaussian Process Regression in TensorFlow Probability Notebook not working

When running the tutorial notebook Gaussian Process Regression in TensorFlow Probability in Google Colab I run into
the following issue:

I execute the code cells in order, without changing any code. The cell containing the following code produces an error.

# Now we optimize the model parameters.
num_iters = 1000
optimizer = tf.optimizers.Adam(learning_rate=.01)

# Store the likelihood values during training, so we can plot the progress
lls_ = np.zeros(num_iters, np.float64)
for i in range(num_iters):
  with tf.GradientTape() as tape:
    loss = -target_log_prob(amplitude_var, length_scale_var,
                            observation_noise_variance_var)
  grads = tape.gradient(loss, trainable_variables)
  optimizer.apply_gradients(zip(grads, trainable_variables))
  lls_[i] = loss

print('Trained parameters:')
print('amplitude: {}'.format(amplitude_var._value().numpy()))
print('length_scale: {}'.format(length_scale_var._value().numpy()))
print('observation_noise_variance: {}'.format(observation_noise_variance_var._value().numpy()))

The error message is:

ValueError                                Traceback (most recent call last)
<ipython-input-81-0b42fdbef836> in <module>()
     10                             observation_noise_variance_var)
     11   grads = tape.gradient(loss, trainable_variables)
---> 12   optimizer.apply_gradients(zip(grads, trainable_variables))
     13   lls_[i] = loss
     14 

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/utils.py in filter_empty_gradients(grads_and_vars)
     74   if not filtered:
     75     raise ValueError("No gradients provided for any variable: %s." %
---> 76                      ([v.name for _, v in grads_and_vars],))
     77   if vars_with_empty_grads:
     78     logging.warning(

ValueError: No gradients provided for any variable: ['amplitude:0', 'length_scale:0', 'observation_noise_variance_var:0'].

After consulting Stack Overflow I tried adding tape.watch(trainable_variables), but this did not change anything.

Might someone be able to help figure out why this is happening?

Reproduce the Gaussian Process Regression in TensorFlow Probability notebook in Google Colab, running the cells in order. Start at the optimization cell shown in the report and investigate why gradients for the three listed variables are empty. Done means the notebook trains and prints the parameters without the reported ValueError.